School of Mathematics, Statistics & Physics, Newcastle University, Tyne and Wear, UK.
Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Tyne and Wear, UK.
Stat Med. 2022 Jul 10;41(15):2908-2922. doi: 10.1002/sim.9393. Epub 2022 Apr 10.
The development of a new diagnostic test ideally follows a sequence of stages which, among other aims, evaluate technical performance. This includes an analytical validity study, a diagnostic accuracy study, and an interventional clinical utility study. In this article, we propose a novel Bayesian approach to sample size determination for the diagnostic accuracy study, which takes advantage of information available from the analytical validity stage. We utilize assurance to calculate the required sample size based on the target width of a posterior probability interval and can choose to use or disregard the data from the analytical validity study when subsequently inferring measures of test accuracy. Sensitivity analyses are performed to assess the robustness of the proposed sample size to the choice of prior, and prior-data conflict is evaluated by comparing the data to the prior predictive distributions. We illustrate the proposed approach using a motivating real-life application involving a diagnostic test for ventilator associated pneumonia. Finally, we compare the properties of the approach against commonly used alternatives. The results show that, when suitable prior information is available, the assurance-based approach can reduce the required sample size when compared to alternative approaches.
新诊断测试的开发理想情况下遵循一系列阶段，除其他目标外，还评估技术性能。这包括分析有效性研究、诊断准确性研究和干预临床实用性研究。在本文中，我们提出了一种新颖的贝叶斯方法来确定诊断准确性研究的样本量，该方法利用了分析有效性阶段提供的信息。我们利用保证来根据后验概率区间的目标宽度计算所需的样本量，并且可以选择在随后推断测试准确性度量时使用或忽略分析有效性研究的数据。进行敏感性分析以评估建议的样本量对先验选择的稳健性，并且通过将数据与先验预测分布进行比较来评估先验-数据冲突。我们使用涉及呼吸机相关性肺炎诊断测试的一个有启发性的实际应用来说明所提出的方法。最后，我们将该方法的性质与常用的替代方法进行了比较。结果表明，当有合适的先验信息时，与替代方法相比，基于保证的方法可以减少所需的样本量。
